Atmospheric Correction of Satellite Ocean-Color Imagery During the PACE Era.

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will carry into space the Ocean Color Instrument (OCI), a spectrometer measuring at 5nm spectral resolution in the ultraviolet (UV) to near infrared (NIR) with additional spectral bands in the shortwave infrared (SWIR), and two multi-angle polarimeters that will overlap the OCI spectral range and spatial coverage, i. e., the Spectrometer for Planetary Exploration (SPEXone) and the Hyper-Angular Rainbow Polarimeter (HARP2). These instruments, especially when used in synergy, have great potential for improving estimates of water reflectance in the post Earth Observing System (EOS) era. Extending the top-of-atmosphere (TOA) observations to the UV, where aerosol absorption is effective, adding spectral bands in the SWIR, where even the most turbid waters are black and sensitivity to the aerosol coarse mode is higher than at shorter wavelengths, and measuring in the oxygen A-band to estimate aerosol altitude will enable greater accuracy in atmospheric correction for ocean color science. The multi-angular and polarized measurements, sensitive to aerosol properties (e.g., size distribution, index of refraction), can further help to identify or constrain the aerosol model, or to retrieve directly water reflectance. Algorithms that exploit the new capabilities are presented, and their ability to improve accuracy is discussed. They embrace a modern, adapted heritage two-step algorithm and alternative schemes (deterministic, statistical) that aim at inverting the TOA signal in a single step. These schemes, by the nature of their construction, their robustness, their generalization properties, and their ability to associate uncertainties, are expected to become the new standard in the future. A strategy for atmospheric correction is presented that ensures continuity and consistency with past and present ocean-color missions while enabling full exploitation of the new dimensions and possibilities. Despite the major improvements anticipated with the PACE instruments, gaps/issues remain to be filled/tackled. They include dealing properly with whitecaps, taking into account Earth-curvature effects, correcting for adjacency effects, accounting for the coupling between scattering and absorption, modeling accurately water reflectance, and acquiring a sufficiently representative dataset of water reflectance in the UV to SWIR. Dedicated efforts, experimental and theoretical, are in order to gather the necessary information and rectify inadequacies. Ideas and solutions are put forward to address the unresolved issues. Thanks to its design and characteristics, the PACE mission will mark the beginning of a new era of unprecedented accuracy in ocean-color radiometry from space.

Initial derivation of diagnostic clusters combining history elements and physical examination tests for symptomatic knee osteoarthritis.

INTRODUCTION: The aim of the present study was to assess the validity of clusters combining history elements and physical examination tests to diagnose symptomatic knee osteoarthritis (SOA) compared with other knee disorders. METHODS: This was a prospective diagnostic accuracy study, in which 279 consecutive patients consulting for a knee complaint were assessed. History elements and standardized physical examination tests were obtained independently by a physiotherapist and compared with an expert physician's composite diagnosis, including clinical examination and imaging. Recursive partitioning was used to develop diagnostic clusters for SOA. Diagnostic accuracy measures were calculated, including sensitivity, specificity, and positive and negative likelihood ratios (LR+/-), with associated 95% confidence intervals (CIs). RESULTS: A total of 129 patients had a diagnosis of SOA (46.2%). Most cases (76%) had combined tibiofemoral and patellofemoral knee OA and 63% had radiological Kellgren-Lawrence grades of 2 or 3. Different combinations of history elements and physical examination tests were used in clusters accurately to discriminate SOA from other knee disorders. These included age of patients, body mass index, presence of valgus/varus knee misalignment, palpable knee crepitus and limited passive knee extension. Two clusters to rule in SOA reached an LR+ of 13.6 (95% CI 6.5 to 28.4) and three clusters to rule out SOA reached an LR- of 0.11 (95% CI 0.06 to 0.20). DISCUSSION: Diagnostic clusters combining history elements and physical examination tests were able to support the differential diagnosis of SOA compared with various knee disorders without relying systematically on imaging. This could support primary care clinicians' role in the efficient management of these patients.

Validity of Combining History Elements and Physical Examination Tests to Diagnose Patellofemoral Pain.

OBJECTIVE: To assess the validity of diagnostic clusters combining history elements and physical examination tests to diagnose or exclude patellofemoral pain (PFP). DESIGN: Prospective diagnostic study. SETTINGS: Orthopedic outpatient clinics, family medicine clinics, and community-dwelling. PARTICIPANTS: Consecutive patients (N=279) consulting one of the participating orthopedic surgeons (n=3) or sport medicine physicians (n=2) for any knee complaint. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: History elements and physical examination tests were obtained by a trained physiotherapist blinded to the reference standard: a composite diagnosis including both physical examination tests and imaging results interpretation performed by an expert physician. Penalized logistic regression (least absolute shrinkage and selection operator) was used to identify history elements and physical examination tests associated with the diagnosis of PFP, and recursive partitioning was used to develop diagnostic clusters. Diagnostic accuracy measures including sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios with associated 95% confidence intervals (CIs) were calculated. RESULTS: Two hundred seventy-nine participants were evaluated, and 75 had a diagnosis of PFP (26.9%). Different combinations of history elements and physical examination tests including the age of participants, knee pain location, difficulty descending stairs, patellar facet palpation, and passive knee extension range of motion were associated with a diagnosis of PFP and used in clusters to accurately discriminate between individuals with PFP and individuals without PFP. Two diagnostic clusters developed to confirm the presence of PFP yielded a positive likelihood ratio of 8.7 (95% CI, 5.2-14.6) and 3 clusters to exclude PFP yielded a negative likelihood ratio of .12 (95% CI, .06-.27). CONCLUSIONS: Diagnostic clusters combining common history elements and physical examination tests that can accurately diagnose or exclude PFP compared to various knee disorders were developed. External validation is required before clinical use.

Remote sensing of phytoplankton chlorophyll-a concentration by use of ridge function fields.

A methodology is presented for retrieving phytoplankton chlorophyll-a concentration from space. The data to be inverted, namely, vectors of top-of-atmosphere reflectance in the solar spectrum, are treated as explanatory variables conditioned by angular geometry. This approach leads to a continuum of inverse problems, i.e., a collection of similar inverse problems continuously indexed by the angular variables. The resolution of the continuum of inverse problems is studied from the least-squares viewpoint and yields a solution expressed as a function field over the set of permitted values for the angular variables, i.e., a map defined on that set and valued in a subspace of a function space. The function fields of interest, for reasons of approximation theory, are those valued in nested sequences of subspaces, such as ridge function approximation spaces, the union of which is dense. Ridge function fields constructed on synthetic yet realistic data for case I waters handle well situations of both weakly and strongly absorbing aerosols, and they are robust to noise, showing improvement in accuracy compared with classic inversion techniques. The methodology is applied to actual imagery from the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS); noise in the data are taken into account. The chlorophyll-a concentration obtained with the function field methodology differs from that obtained by use of the standard SeaWiFS algorithm by 15.7% on average. The results empirically validate the underlying hypothesis that the inversion is solved in a least-squares sense. They also show that large levels of noise can be managed if the noise distribution is known or estimated.